Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userFeedback Scheduling: An Event-Driven Paradigm
285
0
0.0
(
0
)
Added by
Feng Xia
Publication date
2008
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Embedded computing systems today increasingly feature resource constraints and workload variability, which lead to uncertainty in resource availability. This raises great challenges to software design and programming in multitasking environments. In this paper, the emerging methodology of feedback scheduling is introduced to address these challenges. As a closed-loop approach to resource management, feedback scheduling promises to enhance the flexibility and resource efficiency of various software programs through dynamically distributing available resources among concurrent tasks based on feedback information about the actual usage of the resources. With emphasis on the behavioral design of feedback schedulers, we describe a general framework of feedback scheduling in the context of real-time control applications. A simple yet illustrative feedback scheduling algorithm is given. From a programming perspective, we describe how to modify the implementation of control tasks to facilitate the application of feedback scheduling. An event-driven paradigm that combines time-triggered and event-triggered approaches is proposed for programming of the feedback scheduler. Simulation results argue that the proposed event-driven paradigm yields better performance than time-triggered paradigm in dynamic environments where the workload varies irregularly and unpredictably.
rate research
Read More
Due to the increasing complexity seen in both workloads and hardware resources in state-of-the-art embedded systems, developing efficient real-time schedulers and the corresponding schedulability tests becomes rather challenging. Although close to optimal schedulability performance can be achieved for supporting simple system models in practice, adding any small complexity element into the problem context such as non-preemption or resource heterogeneity would cause significant pessimism, which may not be eliminated by any existing scheduling technique. In this paper, we present LINTS^RT, a learning-based testbed for intelligent real-time scheduling, which has the potential to handle various complexities seen in practice. The design of LINTS^RT is fundamentally motivated by AlphaGo Zero for playing the board game Go, and specifically addresses several critical challenges due to the real-time scheduling context. We first present a clean design of LINTS^RT for supporting the basic case: scheduling sporadic workloads on a homogeneous multiprocessor, and then demonstrate how to easily extend the framework to handle further complexities such as non-preemption and resource heterogeneity. Both application and OS-level implementation and evaluation demonstrate that LINTS^RT is able to achieve significantly higher runtime schedulability under different settings compared to perhaps the most commonly applied schedulers, global EDF, and RM. To our knowledge, this work is the first attempt to design and implement an extensible learning-based testbed for autonomously making real-time scheduling decisions.
With traditional open-loop scheduling of network resources, the quality-of-control (QoC) of networked control systems (NCSs) may degrade significantly in the presence of limited bandwidth and variable workload. The goal of this work is to maximize the overall QoC of NCSs through dynamically allocating available network bandwidth. Based on codesign of control and scheduling, an integrated feedback scheduler is developed to enable flexible QoC management in dynamic environments. It encompasses a cascaded feedback scheduling module for sampling period adjustment and a direct feedback scheduling module for priority modification. The inherent characteristics of priority-driven control networks make it feasible to implement the proposed feedback scheduler in real-world systems. Extensive simulations show that the proposed approach leads to significant QoC improvement over the traditional open-loop scheduling scheme under both underloaded and overloaded network conditions.
In this ongoing work, we are interested in multiprocessor energy efficient systems, where task durations are not known in advance, but are know stochastically. More precisely, we consider global scheduling algorithms for frame-based multiprocessor stochastic DVFS (Dynamic Voltage and Frequency Scaling) systems. Moreover, we consider processors with a discrete set of available frequencies.
When integrating hard, soft and non-real-time tasks in general purpose operating systems, it is necessary to provide temporal isolation so that the timing properties of one task do not depend on the behaviour of the others. However, strict budget enforcement can lead to inefficient use of the computational resources in the presence of tasks with variable workload. Many resource reclaiming algorithms have been proposed in the literature for single processor scheduling, but not enough work exists for global scheduling in multiprocessor systems. In this report, we propose two reclaiming algorithms for multiprocessor global scheduling and we prove their correctness.
Myopic is a hard real-time process scheduling algorithm that selects a suitable process based on a heuristic function from a subset (Window)of all ready processes instead of choosing from all available processes, like original heuristic scheduling algorithm. Performance of the algorithm significantly depends on the chosen heuristic function that assigns weight to different parameters like deadline, earliest starting time, processing time etc. and the sizeof the Window since it considers only k processes from n processes (where, k<= n). This research evaluates the performance of the Myopic algorithm for different parameters to demonstrate the merits and constraints of the algorithm. A comparative performance of the impact of window size in implementing the Myopic algorithm is presented and discussed through a set of experiments.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
